Type de document	Thèse/Dissertation
Auteur	El Ghouch, Anouar
URN	BelnUcetd-09262007-123927
Langue	Anglais/English
Titre	Nonparametric statistical inference for dependent censored data
Intitulé du diplôme	STAT 3 - Doctorat en sciences (statistique)
Département/Domaine	EUEN/STAT - Institut de statistique
Adresse e-mail du département	Marguerite-Marie.Hanon@uclouvain.be
Jury	Nom Titre Gijbels, Irène Membre du jury/Committee Member McKeague, Ian Membre du jury/Committee Member Van Bellegem, Sébastien Membre du jury/Committee Member Von Sachs, Rainer Président du jury/Committee Chair Van Keilegom, Ingrid Promoteur/Director
Mots-clés	Kernel smoothing Local linear Blocking Quantile regression Survival analysis Nonparametric regression Mean regression Mixing sequences Censoring Kaplan-Meier integral
Date de défense	2007-10-05
Type d'accès	unrestricted
Résumé A frequent problem that appears in practical survival data analysis is censoring. A censored observation occurs when the observation of the event time (duration or survival time) may be prevented by the occurrence of an earlier competing event (censoring time). Censoring may be due to different causes. For example, the loss of some subjects under study, the end of the follow-up period, drop out or the termination of the study and the limitation in the sensitivity of a measurement instrument. The literature about censored data focuses on the i.i.d. case. However in many real applications the data are collected sequentially in time or space and so the assumption of independence in such case does not hold. Here we only give some typical examples from the literature involving correlated data which are subject to censoring. In the clinical trials domain it frequently happens that the patients from the same hospital have correlated survival times due to unmeasured variables like the quality of the hospital equipment. Censored correlated data are also a common problem in the domain of environmental and spatial (geographical or ecological) statistics. In fact, due to the process being used in the data sampling procedure, e.g. the analytical equipment, only the measurements which exceed some thresholds, for example the method detection limits or the instrumental detection limits, can be included in the data analysis. Many other examples can also be found in other fields like econometrics and financial statistics. Observations on duration of unemployment e.g., may be right censored and are typically correlated. When the data are not independent and are subject to censoring, estimation and inference become more challenging mathematical problems with a wide area of applications. In this context, we propose here some new and flexible tools based on a nonparametric approach. More precisely, allowing dependence between individuals, our main contribution to this domain concerns the following aspects. First, we are interested in developing more suitable confidence intervals for a general class of functionals of a survival distribution via the empirical likelihood method. Secondly, we study the problem of conditional mean estimation using the local linear technique. Thirdly, we develop and study a new estimator of the conditional quantile function also based on the local linear method. In this dissertation, for each proposed method, asymptotic results like consistency and asymptotic normality are derived and the finite sample performance is evaluated in a simulation study.
Autre version
Fichiers	Nom du fichier       Taille       Temps de chargement évalué (Heures:Minutes:Secondes)     28.8 Modem   56K Modem   Acces haute-vitesse    pre_these.pdf 848.84 Kb 00:03:55 00:02:01 00:00:04